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Digital terrain analysis (DTA) is one of the most important contents in the research of geographical information science (GIS). However, on the basis of the digital elevation model (DEM), many problems exist in the current research of DTA in geomorphological studies. For instance, the current DTA research appears to be focused more on morphology, phenomenon, and modern surface rather than mechanism, process, and underlying terrain. The current DTA research needs to be urgently transformed from the study of landform morphology to one focusing on landform process and mechanism. On this basis, this study summarizes the current research status of geomorphology-oriented DTA and systematically reviews and analyzes the research about the knowledge of geomorphological ontology, terrain modeling, terrain derivative calculation, and terrain analytical methods. With the help of DEM data, DTA research has the advantage of carrying out geomorphological studies from the perspective of surface morphology. However, the study of DTA has inherent defects in terms of data expression and analytic patterns. Thus, breakthroughs in basic theories and key technologies are necessary. Moreover, scholars need to realize that DTA research must be transformed from phenomenon to mechanism, from morphology to process, and from terrain to landform. At present, the research development of earth science has reached the critical stage in which the DTA research should focus more on geomorphological ontology. Consequently, this study proposes several prospects of geomorphology-oriented DTA from the aspects of value-added DEM data model, terrain derivatives and their spatial relations, and macro-terrain analysis. The study of DTA based on DEM is at a critical period along with the issue on whether the current GIS technology can truly support the development of geography. The research idea of geomorphology-oriented DTA is expected to be an important exploration and practice in the field of GIS.

Currently, the theory and methodology of digital terrain analysis (DTA) has been well developed. However, this technique has not been widely applied in the research of loess landslides in China. This study investigated the application of DTA on loess landslides with the high-resolution terrain data obtained from low-cost unmanned aerial vehicles (UAVs). Taking a high-speed and long-runout landslide occurring on the Bailu Loess Tableland, a typical landform type on the Loess Plateau, as an example, we illustrated the fundamental characteristics and spatial patterns of the landslide from various perspectives and performed hydrology analysis, geomorphic change detection, hypsometric integral (HI) and stability analysis, morphology analysis, and structure analysis. The results prove that the DTA methodology cannot only advance understanding of the geomorphology and structure of landslides and detect geomorphic change but also reveal the evolution principles of landforms and demonstrate unique advantages in the prediction of the internal stability of landslides. In conclusion, the DTA methods adopted in this paper are useful to better understand loess landslide and its relationship with geomorphologic evolution.

Terrain characteristics and their evaluation usually come under geomorphological study and more particularly the applied geomorphological study (Prasad & Sarkar 2011). Foothills are a geographically defined zone having a gradual increase in elevation at the base of a mountain or hill range. Detailed assessment of the present terrain parameters of the study area using GIS is significant as it shows the influence on the landscape of the area. It is a prerequisite in effective management of the impact of transition upon the landscape and its natural resources for sustainable management. In the study, an attempt has been made to delineate the foothill belt of the Assam-Meghalaya border in Kamrup District, Assam using Geographical Information system (GIS), and remote sensing techniques. Datasets available from USGS Earth Explorer, i.e. Shuttle Radar Topographic Mission (SRTM) and Digital Elevation Model (DEM) are used for analyzing the elevation, contour, slope, and terrain characteristics. The present study aims at getting an information archive of the geomorphological and terrain characteristics of the Assam-Meghalaya foothills in Kamrup District, Assam, and its spatio-temporal variation.

Geomorphometry, the science of digital terrain analysis （DTA）, is an important focus of research in both geomorphology and geographical information science （GIS）. Given that 70% of China is mountainous, geomorphological research is popular among Chinese schol- ars, and the development of GIS over the last 30 years has led to significant advances in geomorphometric research. In this paper, we review Chinese progress in geomorphometry based on the published literature. There are three major areas of progress： digital terrain modelling methods, DTA methods, and applications of digital terrain models （DTMs）. First, traditional vector- and raster-based terrain modelling methods, including the assessment of uncertainty, have received widespread attention. New terrain modelling methods such as unified raster and vector, high-fidelity, and real-time dynamic geographical scene modelling have also attracted research attention and are now a major focus of digital terrain modelling research. Second, in addition to the popular DTA methods based on topographical derivatives geomorphological features, and hydrological factors extracted from DTMs, DTA methods have been extended to include analyses of the structure of underlying strata, ocean surface features and even socioeconomic spatial structures. Third, DTMs have been applied to fields including global climate change, analysis of various typical regions, lunar surface and other related fields. Clearly, Chinese scholars have made significant progress in geomorphometry. Chinese scholars have had the greatest international impact in areas including high-fidelity digital terrain modelling and DTM-based regional geomorphological analysis, particularly in the Loess Plateau and the Tibetan Plateau regions.

Terrain rendering is an important issue in Geographic Information Systems and other fields. During large-scale, real-time terrain rendering, complex terrain structure and an increasing amount of data decrease the smoothness of terrain rendering. Existing rendering methods rarely use the features of terrain to optimize terrain rendering. This paper presents a method to increase rendering performance through precomputing roughness and self-occlusion information making use of GIS-based Digital Terrain Analysis. Our method is based on GPU tessellation. We use quadtrees to manage patches and take surface roughness in Digital Terrain Analysis as a factor of Levels of Detail (LOD) selection. Before rendering, we first regularly partition the terrain scene into view cells. Then, for each cell, we calculate its potential visible patch set (PVPS) using a visibility analysis algorithm. After that, A PVPS Image Pyramid is built, and each LOD level has its corresponding PVPS. The PVPS Image Pyramid is stored on a disk and is read into RAM before rendering. Based on the PVPS Image Pyramid and the viewpoint’s position, invisible terrain areas that are not culled through view frustum culling can be dynamically culled. We use Digital Elevation Model (DEM) elevation data of a square area in Henan Province to verify the effectiveness of this method. The experiments show that this method can increase the frame rate compared with other methods, especially for lower camera flight heights.

Flood mapping is a vital component for sustainable land use in flood‐prone areas. Due to the frequency of flood events, local authorities demand effective yet simple methods for the preliminary identification of flood‐prone areas at large scales to subsequently define mitigation strategies. We focus here on the workflow GeoFlood, a parsimonious model which uses only high‐resolution Digital Terrain Models (DTMs) to define the geomorphological and hydrological information necessary for flood inundation mapping, thus allowing for large‐scale simulations at a reasonable computational cost. The purpose of the present study is to investigate the conditions under which GeoFlood is able to correctly reproduce inundation scenarios (with an assigned return period) and their flooding characteristics. Specifically, we analyze its performance over a highly urbanized area, the mid‐lower portion of the Tiber River (Italy). We simulated the 200‐year return period scenario and compared the results to those provided by the local authority. A sensitivity analysis is performed to quantify the influence of the main geometric and hydraulic parameters involved. Results show that GeoFlood produces rapid flood estimation than can be used in support of standard costly methods over large scales, quickly pointing out the critical flood‐prone areas, with consideration of all the uncertainties involved.

Terrain texture analysis is an important method of digital terrain analysis in quantitative geomorphological research and in the exploration of the spatial heterogeneity and autocorrelation of terrain features. However, a major issue often neglected in previous studies is the calculation unit of the terrain texture, that is, the stability analysis unit. As the test size increases, the derived terrain textures become increasingly similar so that their differences can be ignored. The test size of terrain texture is defined as the stability analysis unit. This study randomly selected 48 areas within the Loess Plateau in northern Shaanxi in China as the study sites and used the gray level co-occurrence matrix to calculate the terrain texture. The stability analysis unit of the terrain texture was then extracted, and its spatial distribution pattern in the Loess Plateau was studied using spatial interpolation method. Four terrain texture metrics, i.e., homogeneity, energy, correlation, and contrast, were extracted on the basis of the stability analysis unit, and the spatial variation patterns of these parameters were studied. Results showed that the spatial distribution pattern and the terrain texture metrics reflected a trend of high–low–high from north to south, which correlated with the spatial distribution of the landforms at the Loess Plateau. In addition, the terrain texture measures was significantly correlated with the terrain factors of gully density and slope, and this relationship showed that terrain texture measures based on the stability analysis unit could reflect the basic characteristics of terrain morphology. The stability analysis unit provided a reasonable analytical scale for terrain texture analysis and could be used as a measure of the regional topography to accurately describe basic terrain characteristics.

Smart construction technology integrates artificial intelligence, Internet of Things, UAVs, and building information modeling to improve productivity and quality in construction. In road embankment earthworks, ground compaction quality is critical for structural stability and maintenance. This study proposes a methodology combining UAV photogrammetry with intelligent compaction quality management systems to evaluate surface flatness and compaction homogeneity in real-time. High-resolution UAV images were used to generate digital elevation models, from which surface roughness was extracted using terrain element analysis and fast Fourier transform. Local terrain changes were interpreted through contour gradient, outline gradient, and tangential gradient curvature analysis. Field tests were conducted at a pilot site using a vibratory roller, followed by four compaction quality assessments: plate load test, dynamic cone penetration test, light falling weight deflectometer, and compaction meter value. UAV-based flatness analysis revealed that, when surface flatness met the standard, a strong correlation was observed, with results from conventional field tests and intelligent compaction data. The proposed method effectively identified poorly compacted zones and spatial inhomogeneity without interrupting construction. These findings demonstrate that UAV-based terrain analysis can serve as a nondestructive real-time monitoring tool and contribute to automated quality control in smart construction environments.

China's Chang'e‐4 (CE‐4) probe will explore the South Pole‐Aitken basin in late 2018. Its preselected landing area is located on the southeastern floor of the Von Kármán crater. Landing experience of China's Chang'e‐3 probe may not be applied to CE‐4 mission directly because the topography of lunar farside is more rugged. Moreover, due to scale dependence and smooth effects, the previous topographic studies derived from digital elevation model data with lower resolution cannot represent the meter‐scale topographic features. Because of low time and space complexities in lunar images, image segmentation algorithm is especially suitable for recognizing lunar features. We divided the narrow angle camera (NAC) mosaic image into three parts: dark area, bright area, and flat area, based on a double‐threshold Otsu method. The first two parts corresponded to the undulating areas (positive and negative terrains). And then we calculated the flat area percentage (Fap) of the previous successful lunar landing missions (including Chang'e‐3, Apollo, Surveyor, and Luna series) and obtained the Fap threshold (>0.6) for lunar safe landing. According to Fap map (~1.5 m per pixel) of CE‐4 preselected landing area, the divided square grids with a size of 0.01° can be classified into safe grids (Fap > 0.6) and unsafe grids (Fap ≤ 0.6). The CE‐4 preselected landing areas can be greatly reduced to five potential landing areas. In the last, we proposed a route planning method, which took both the distance and security into account, for CE‐4 rover to dynamically generate a safe and short route between current grid and target grid. Plain Language Summary China's Chang'e‐4 (CE‐4) probe will land on the South Pole‐Aitken basin at the end of 2018. Its preselected landing area (45–46°S, 176.4–178.8°E) is located on the southeastern floor of the Von Kármán crater. It is a very large area, and there are many small (meter‐scale) topographic features such as rocks, craters, ridges, and troughs. Therefore, it is necessary to narrow the scope and choose the safe landing areas from the preselected landing area. The flat area percentage (Fap) is an important factor to measure the safety of landing area. Based on an image segmentation method, we extracted Faps in square grids with a size of 0.01°and then the CE‐4 landing areas were reduced to five safe areas. After CE‐4 lands at the safe site, a rover will be released to travel around. We proposed a path planning method for CE‐4 rover moving from current grid to target grid. This method took both the distance and security into account to find out the best path with safety and short distance. Key Points NAC mosaic image was divided into three parts with a resolution of ~1.5 m using a double‐threshold Otsu method The range of CE‐4 preselected landing area was greatly reduced to five potential landing areas according to the flat area percentage A route planning method was proposed to dynamically generate a safe and short route between current grid and target grid for CE‐4 rover

Finnish arable land is typically located on flat areas, where the fields are mostly drained and sub-drained to control the water tables. These areas are highly susceptible to nutrient loss, which affects the water quality of rivers and lakes. Therefore, it is very important to understand current landscape pattern and processes controlling water quality, not only identifying factors affecting it, but also identifying strategies and restoring areas for mitigation. We studied linkage of 21 years (1990–2011) of water quality (WQ) data from 16 agricultural watersheds, using landscape indices at three functional scales: watershed-wide, saturation-excess zone, and riparian zone (of varying widths). The hydro-biogeochemical functional areas of watershed were obtained by digital terrain analysis. Statistical analyses by generalized linear model and multivariate redundancy analysis indicated that the fraction of watershed in agricultural use was linked to most of the studied water quality variables. The relationships varied across the seasons: they were strongest during high flow periods (spring and autumn) when also highest nutrient losses occur. Total suspended sediment concentrations were linked to critical source areas. Riparian vegetation index was important explaining nitrate concentrations in autumn. Terrain-based mapping of hydro-biogeochemical functional areas provides a rapid identification of potential sites to mitigate diffuse nutrient pollution, particularly in riparian zones.